Spatial generalization and aggregation of massive movement data

Movement data (trajectories of moving agents) are hard to visualize: numerous intersections and overlapping between trajectories make the display heavily cluttered and illegible. It is necessary to use appropriate data abstraction methods. We suggest a method for spatial generalization and aggregation of movement data, which transforms trajectories into aggregate flows between areas. It is assumed that no predefined areas are given. We have devised a special method for partitioning the underlying territory into appropriate areas. The method is based on extracting significant points from the trajectories. The resulting abstraction conveys essential characteristics of the movement. The degree of abstraction can be controlled through the parameters of the method. We introduce local and global numeric measures of the quality of the generalization, and suggest an approach to improve the quality in selected parts of the territory where this is deemed necessary. The suggested method can be used in interactive visual exploration of movement data and for creating legible flow maps for presentation purposes.     

The train driver recovery problem—A set partitioning based model and solution method

The need to recover a train driver schedule occurs during major disruptions in the daily railway operations. Based on data from the Danish passenger railway operator DSB S-tog A/S, a solution method to the train driver recovery problem (TDRP) is developed. The TDRP is formulated as a set partitioning problem. We define a disruption neighbourhood by identifying a small set of drivers and train tasks directly affected by the disruption. Based on the disruption neighbourhood, the TDRP model is formed and solved. If the TDRP solution provides a feasible recovery for the drivers within the disruption neighbourhood, we consider that the problem is solved. However, if a feasible solution is not found, the disruption neighbourhood is expanded by adding further drivers or increasing the recovery time period. Fractional solutions to the LP relaxation of the TDRP are resolved with a constraint branching strategy using the depth-first search of the Branch & Bound tree. The LP relaxation of the TDRP possesses strong integer properties. We present test scenarios generated from the historical real-life operations data of DSB S-tog A/S. The numerical results show that all but one tested instances produce integer solutions to the LP relaxation of the TDRP and solutions are found within a few seconds.     

Parallel batch scheduling of equal-length jobs with release and due dates

In this paper we study parallel batch scheduling problems with bounded batch capacity and equal-length jobs in a single and parallel machine environment. It is shown that the feasibility problem 1|p-batch,b<n,r _j ,p _j =p,C _j ≤d _j |− can be solved in O(n ²) time and that the problem of minimizing the maximum lateness can be solved in O(n ²log n) time. For the parallel machine problem P|p-batch,b<n,r _j ,p _j =p,C _j ≤d _j |− an O(n ³log n)-time algorithm is provided, which can also be used to solve the problem of minimizing the maximum lateness in O(n ³log ² n) time.     

Urinary proteomic analysis of chronic allograft nephropathy

The pathogenesis of progressive renal allograft injury, which is termed chronic allograft nephropathy (CAN), remains obscure and is currently defined by histology. Prospective protocol-biopsy trials have demonstrated that clinical and standard laboratory tests are insufficiently sensitive indicators of the development and progression of CAN. The study aim was to determine if CAN could be characterized by urinary proteomic data and identify the proteins associated with disease. The urinary proteome of 75 renal transplant recipients and 20 healthy volunteers was analyzed using surface enhanced laser desorption and ionization MS. Patients could be classified into subgroups with normal histology and Banff CAN grades 2-3 with a sensitivity of 86% and a specificity of 92% by applying the classification algorithm Adaboost to urinary proteomic data. Several urinary proteins associated with advanced CAN were identified including α1-microglobulin, β2-microglobulin, prealbumin, and endorepellin, the antiangiogenic C-terminal fragment of perlecan. Increased urinary endorepellin was confirmed by ELISA and increased tissue expression of the endorepellin/perlecan ratio by immunofluoresence analysis of renal biopsies. In conclusion, analysis of urinary proteomic data has further characterized the more severe CAN grades and identified urinary endorepellin, as a potential biomarker of advanced CAN.     

On techniques for angle compensation in nonideal iris recognition.

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word "nonideal" is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.     

Solving large multiple-depot multiple-vehicle-type bus scheduling problems in practice

We consider the multiple-depot multiple-vehicle-type scheduling problem (MDVSP) which arises in public transport bus companies and aims to assign buses to cover a given set of timetabled trips with consideration of practical requirements, such as multiple depots and vehicle types as well as depot capacities. An optimal schedule is characterized by minimal fleet size and minimal operational costs including costs for empty movements and waiting time. It is well-known that the MDVSP is NP-hard.Although progress has recently been made in solving large practical MDVSP to optimality with time-space network models, current optimization technology sets limits to the model size that can be solved. In order to approach very large practical instances we propose a two-phase method which produces close to optimal solutions. This modeling approach enables us to solve real-world problem instances with thousands of scheduled trips by direct application of standard optimization software. Furthermore, we introduce the concept of depot groups for the case that a bus may return in the evening into another depot than where it started in the morning.     

Exploratory spatio-temporal visualization: an analytical review

Current software tools for visualization of spatio-temporal data, on the one hand, utilize the opportunities provided by modern computer technologies, on the other hand, incorporate the legacy from the conventional cartography. We have considered existing visualization-based techniques for exploratory analysis of spatio-temporal data from two perspectives: (1) what types of spatio-temporal data they are applicable to; (2) what exploratory tasks they can potentially support.The technique investigation has been based on an operational typology of spatio-temporal data and analytical tasks we specially devised for this purpose. The result of the study is a structured inventory of existing exploratory techniques related to the types of data and tasks they are appropriate for. This result is potentially helpful for data analysts—users of geovisualization tools: it provides guidelines for selection of proper exploratory techniques depending on the characteristics of data to analyze and the goals of analysis. At the same time the inventory as well as the suggested typology of tasks could be useful for tool designers and developers of various domain-specific geovisualization applications. The designers can, on the one hand, see what task types are insufficiently supported by the existing tools and direct their creative activities towards filling the gaps, on the other hand, use the techniques described as basic elements for building new, more sophisticated ones. The application developers can, on the one hand, use the task and data typology in the analysis of potential user needs, on the other hand, appropriately select and combine existing tools in order to satisfy these needs.     

Assessment of oxygen levels in convenience‐style muscle‐based sous vide products through optical means and impact on shelf‐life stability

An optical oxygen analyser was used in two small‐scale industrial trials to non‐destructively assess the quality of packaged convenience foods and packaging process. Beef lasagne packed under 70% vacuum and cooked under standard sous vide conditions was monitored for residual oxygen, using disposable sensors incorporated in each pack and a benchtop optical oxygen analyser. High levels of residual oxygen close to ambient were determined in the majority of packs after packaging, as opposed to anticipated levels of 4–5%. Residual oxygen was monitored over product shelf‐life (4 weeks at +4–10°C) along with measurement of microbial growth and lipid oxidation in food by conventional destructive methods. Oxygen levels in packs went down to almost zero between weeks 2 and 3 indicating deterioration of packaged product. Correlation between oxygen profiles and the rate of microbial growth and lipid oxidation was established. The optical oxygen sensor system was shown to provide valuable information about performance of the packaging process, product storage conditions and food quality in a convenient and cost‐efficient fashion and non‐destructively. Copyright © 2004 John Wiley & Sons, Ltd.     

Hyaluronic Acid Scaffolds for Loco-Regional Therapy in Nervous System Related Disorders

Hyaluronic acid (HA) is a Glycosaminoglycan made of disaccharide units containing N-acetyl-D-glucosamine and glucuronic acid. Its molecular mass can reach 10 MDa and its physiological properties depend on its polymeric property, polyelectrolyte feature and viscous nature. HA is a ubiquitous compound found in almost all biological tissues and fluids. So far, HA grades are produced by biotechnology processes, while in the human organism it is a major component of the extracellular matrix (ECM) in brain tissue, synovial fluid, vitreous humor, cartilage and skin. Indeed, HA is capable of forming hydrogels, polymer crosslinked networks that are very hygroscopic. Based on these considerations, we propose an overview of HA-based scaffolds developed for brain cancer treatment, central and peripheral nervous systems, discuss their relevance and identify the most successful developed systems.     

The Role of Telomerase in Breast Cancer’s Response to Therapy

Currently, breast cancer appears to be the most widespread cancer in the world and the most common cause of cancer deaths. This specific type of cancer affects women in both developed and developing countries. Prevention and early diagnosis are very important factors for good prognosis. A characteristic feature of cancer cells is the ability of unlimited cell division, which makes them immortal. Telomeres, which are shortened with each cell division in normal cells, are rebuilt in cancer cells by the enzyme telomerase, which is expressed in more than 85% of cancers (up to 100% of adenocarcinomas, including breast cancer). Telomerase may have different functions that are related to telomeres or unrelated. It has been shown that high activity of the enzyme in cancer cells is associated with poor cell sensitivity to therapies. Therefore, telomerase has become a potential target for cancer therapies. The low efficacy of therapies has resulted in the search for new combined and more effective therapeutic methods, including the involvement of telomerase inhibitors and telomerase-targeted immunotherapy.